SoroushVahidi/scidocs huggingface dataset:
Optimal Multi-taxi Dispatch for Mobile Taxi-Hailing Systems
(2016)

Traditional taxi-hailing systems through wireless networks in metropolitan areas allow taxis to compete for passengers chaotically and accidentally, which generally result in inefficiencies, long waiting time and low satisfaction of taxi-hailing passengers. In this paper, we propose a new Mobile Taxi-hailing System (called MTS) based on optimal multi-taxi dispatch, which can be used by taxi service companies (TSCs). Different from the competition modes used in traditional taxi-hailing systems, MTS assigns vacant taxis to taxi-hailing passengers proactively. For the taxi dispatch problem in MTS, we define a system utility function, which involves the total net profits of taxis and waiting time of passengers. Moreover, in the utility function, we take into consideration the various classes of taxis with different resource configurations, and the cost associated with taxis' empty travel distances. Our goal is to maximize the system utility function, restricted by the individual net profits of taxis and the passengers' requirements for specified classes of taxis. To solve this problem, we design an optimal algorithm based on the idea of Kuhn-Munkres (called KMBA), and prove the correctness and optimality of the proposed algorithm. Additionally, we demonstrate the significant performances of our algorithm through extensive simulations.
Learning Multilingual Word Representations using a Bag-of-Words Autoencoder
(2014)

Recent work on learning multilingual word representations usually relies on the use of word-level alignements (e.g. infered with the help of GIZA++) between translated sentences, in order to align the word embeddings in different languages. In this workshop paper, we investigate an autoencoder model for learning multilingual word representations that does without such word-level alignements. The autoencoder is trained to reconstruct the bag-of-word representation of given sentence from an encoded representation extracted from its translation. We evaluate our approach on a multilingual document classification task, where labeled data is available only for one language (e.g. English) while classification must be performed in a different language (e.g. French). In our experiments, we observe that our method compares favorably with a previously proposed method that exploits word-level alignments to learn word representations.
Digital receivers and transmitters using polyphase filter banks for wireless communications
(2003)

This paper provides a tutorial overview of multichannel wireless digital receivers and the relationships between channel bandwidth, channel separation, and channel sample rate. The overview makes liberal use of figures to support the underlying mathematics. A multichannel digital receiver simultaneously down-convert a set of frequency-division-multiplexed (FDM) channels residing in a single sampled data signal stream. In a similar way, a multichannel digital transmitter simultaneously up-converts a number of baseband signals to assemble a set of FDM channels in a single sampled data signal stream. The polyphase filter bank has become the architecture of choice to efficiently accomplish these tasks. This architecture uses three interacting processes to assemble or to disassemble the channelized signal set. In a receiver, these processes are an input commutator to effect spectral folding or aliasing due to a reduction in sample rate, a polyphase -path filter to time align the partitioned and resampled time series in each path, and a discrete Fourier transform to phase align and separate the multiple baseband aliases. In a transmitter, these same processes operate in a related manner to alias baseband signals to high order Nyquist zones while increasing the sample rate with the output commutator. This paper presents a sequence of simple modifications to sampled data structures based on analog prototype systems to obtain the basic polyphase structure. We further discuss ways to incorporate small modifications in the operation of the polyphase system to accommodate secondary performance requirements. MATLAB simulations of a 10-, 40-, and 50-channel resampling receiver are included in the electronic version of this paper. An animated version of the ten-channel resampling receiver illustrates the time and frequency response of the filter bank when driven by a slowly varying linear FM sweep.
A PASSIVE MILLIMETER-WAVE IMAGER USED FOR CONCEALED WEAPON DETECTION
(2012)

An 8mm-band passive millimeter-wave imager BHU-2D has been developed by Beihang University. This imager is designed for detecting concealed weapons on human body. The imager adopts two-dimensional synthetic aperture interferometric radiometer (SAIR) technique which avoids the radiation risk to human body. Compared with scanning technique, SAIR technique could obtain images of a larger field of view (FOV) while achieving high imaging rate, which are necessary for security monitoring. In this paper, the imaging principle of SAIR is stated firstly. Secondly, background cancellation method designed for BHU-2D is interpreted. The technique is used to reduce the complexity as well as the dimensional requirements of the receiving elements. Thirdly, system configuration is illustrated in detail. Then external point source calibration method is introduced and discussed specifically for security check applications. Finally, imaging experiments on a person with concealed weapon are conducted by which the design and image calibration algorithms are verified. To conclude, experimental results prove that BHU-2D could be employed in security check applications.
Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning
(2015)

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.
Empirical analysis of programming language adoption
(2013)

Some programming languages become widely popular while others fail to grow beyond their niche or disappear altogether. This paper uses survey methodology to identify the factors that lead to language adoption. We analyze large datasets, including over 200,000 SourceForge projects, 590,000 projects tracked by Ohloh, and multiple surveys of 1,000-13,000 programmers.
 We report several prominent findings. First, language adoption follows a power law; a small number of languages account for most language use, but the programming market supports many languages with niche user bases. Second, intrinsic features have only secondary importance in adoption. Open source libraries, existing code, and experience strongly influence developers when selecting a language for a project. Language features such as performance, reliability, and simple semantics do not. Third, developers will steadily learn and forget languages. The overall number of languages developers are familiar with is independent of age. Finally, when considering intrinsic aspects of languages, developers prioritize expressivity over correctness. They perceive static types as primarily helping with the latter, hence partly explaining the popularity of dynamic languages.
Symbolic Music Genre Transfer with CycleGAN
(2018)

Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer. In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer. Evaluations using separate genre classifiers show that the style transfer works well. In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer. Visual and audible results further show the potential of our approach. To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.
An effective way of word-level language identification for code-mixed facebook comments using word-embedding via character-embedding
(2017)

Individuals utilize online networking sites like Facebook and Twitter to express their interests, opinions or reviews. The users used English language as their medium for communication in earlier days. Despite the fact that content can be written in Unicode characters now, people find it easier to communicate by mixing two or more languages together or lean toward writing their native language in Roman script. These types of data are called code-mixed text. While processing such social-media data, recognizing the language of the text is an important task. In this work, we have developed a system for word-level language identification on code-mixed social media text. The work is accomplished for Tamil-English and Malayalam-English code-mixed Facebook comments. The methodology used for the system is a novel approach which is implemented based on features obtained from character-based embedding technique with the context information and uses a machine learning based classifier, Support Vector Machine for training and testing. An accuracy of 93% was obtained for Malayalam-English and 95% for Tamil-English code-mixed text.
Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study
(2017)

BACKGROUND
Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden.


OBJECTIVE
The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy.


METHODS
A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability.


RESULTS
Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants.


CONCLUSIONS
Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.
Evaluating and exploring the MYO ARMBAND
(2016)

Electromyography (EMG) is a technique that studies the electrical activity of the muscle. EMG is the basic component of Myo armband. The MYO ARMBAND is a device which can be used by humans to interact with computers. The myo armband involves the interaction of devices in VE (virtual environment). Working and characteristics of Myo are studied and analyzed. This paper surveys the work on visual analysis of gestures by using Myo armband and the input from keyboard and mouse. We have also performed two tests where participants are allowed to play a game, first by using keyboard and other by using MYO. Discussion and conclusion are also written at the end of this paper.
Multimedia learning in computer science: The effect of fifferent modes of instruction on student understanding
(2008)

In a series of three experimental studies, we investigate the understanding achieved by learners when studying a complex domain in computer science, that is, the subject of memory management concepts in operating systems. We use three different media combinations to explain the subject of memory management concepts - Animation and text, animation and voice and a combination of animation, voice and text. The students who had no prior knowledge on the subject where given a test on recall and transfer knowledge after viewing the treatments. This paper reports the results of the experiment which may provide useful guidance for instructional designers specifically in the area of computer science.
Fast Hydraulic and Thermal Erosion on the GPU Balázs Jákó
(2011)

Computer games, TV series, movies, simulators, and many other computer graphics applications use external scenes where a realistic looking terrain is a vital part of the viewing experience. Creating such terrains is a challenging task. In this paper we propose a method that generates realistic virtual terrains by simulation of hydraulic and thermal erosion on a predefined height field terrain. The model is designed to be executed interactively on parallel architectures like graphics processors.
Channel estimation in millimeter wave MIMO systems with one-bit quantization
(2014)

We develop channel estimation agorithms for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem is formulated as a one-bit compressed sensing problem. We propose a modified EM algorithm that exploits sparsity and has better performance than the conventional EM algorithm. We also present a second solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem. The simulation results show that GAMP can reduce mean squared error in the important low and medium SNR regions.
CRISP-DM 1.0: Step-by-step data mining guide
(2000)

This document describes the CRISP-DM process model, including an introduction to the CRISP-DM methodology, the CRISP-DM reference model, the CRISP-DM user guide and the CRISP-DM reports, as well as an appendix with additional useful and related information. This document and information herein, are the exclusive property of the partners of the CRISP-DM All trademarks and service marks mentioned in this document are marks of their respective owners and are as such acknowledged by the members of the CRISP-DM consortium. Foreword CRISP-DM was conceived in late 1996 by three " veterans " of the young and immature data mining market. DaimlerChrysler (then Daimler-Benz) was already experienced, ahead of most industrial and commercial organizations, in applying data mining in its business operations. SPSS (then ISL) had been providing services based on data mining since 1990 and had launched the first commercial data mining workbench – Clementine – in 1994. NCR, as part of its aim to deliver added value to its Teradata data warehouse customers, had established teams of data mining consultants and technology specialists to service its clients' requirements. At that time, early market interest in data mining was showing signs of exploding into widespread uptake. This was both exciting and terrifying. All of us had developed our approaches to data mining as we went along. Were we doing it right? Was every new adopter of data mining going to have to learn, as we had initially, by trial and error? And from a supplier's perspective, how could we demonstrate to prospective customers that data mining was sufficiently mature to be adopted as a key part of their business processes? A standard process model, we reasoned, non-proprietary and freely available, would address these issues for us and for all practitioners. A year later we had formed a consortium, invented an acronym (CRoss-Industry Standard Process for Data Mining), obtained funding from the European Commission and begun to set out our initial ideas. As CRISP-DM was intended to be industry-, tool-and application-neutral, we knew we had to get input from as wide a range as possible of practitioners and others (such as data warehouse vendors and management consultancies) with a vested interest in data mining. We did this by creating the CRISP-DM Special Interest Group (" The SIG " , as it became known). We launched the SIG by broadcasting an invitation to interested parties to join us in Amsterdam for a day-long …
Translating Collocations for Bilingual Lexicons: A Statistical Approach
(1996)

Collocations are notoriously difficult for non-native speakers to translate, primarily because they are opaque and cannot be translated on a word-by-word basis. We describe a program named Champollion which, given a pair of parallel corpora in two different languages and a list of collocations in one of them, automatically produces their translations. Our goal is to provide a tool for compiling bilingual lexical information above the word level in multiple languages,for different domains. The algorithm we use is based on statistical methods and produces p-word translations of n-word collocations in which n and p need not be the same. For example, Champollion translates make ... decision, employment equity, and stock market into prendre ... d6cision, 6quit6 en mati6re d'emploi, and bourse respectively. Testing Champollion on three years' worth of the Hansards corpus yielded the French translations of 300 collocations for each year, evaluated at 73% accuracy on average. In this paper, we describe the statistical measures used, the algorithm, and the implementation of Champollion, presenting our results and evaluation.
Adaptive RED : An Algorithm for Increasing the Robustness of RED ’ s Active Queue Management
(2001)

The RED active queuemanagement algorithmallows network operatorsto simultaneouslyachieve high throughput and low averagedelay. However, the resulting average queuelength is quite sensiti ve to the level of congestion andto theREDparameter settings,andis thereforenotpredictable in advance. Delay being a major componentof thequalityof servicedeliveredto their customers, network operatorswould naturallylike to have a rougha priori estimateof the averagedelaysin their congestedrouters;to achieve suchpredictableaveragedelayswith RED would requireconstanttuning of the parametersto adjustto currenttraffic conditions. Our goal in this paperis to solve this problemwith minimal changesto the overall RED algorithm. To do so,we revisit theAdaptiveREDproposalof Fenget al. from 1997 [6, 7]. We make several algorithmicmodificationsto this proposal,while leaving thebasicideaintact,andthenevaluateits performanceusingsimulation.Wefind thatthis revisedversionof AdaptiveRED,whichcanbeimplemented asasimpleextensionwithin REDrouters,removesthesensitivity to parametersthat affect RED’s performanceand canreliablyachieve aspecifiedtargetaveragequeuelength in a wide variety of traffic scenarios.Basedon extensi ve simulations,we believe that Adaptive RED is sufficiently robustfor deploymentin routers.
Adaptive generic learning for face recognition from a single sample per person
(2010)

Real-world face recognition systems often have to face the single sample per person (SSPP) problem, that is, only a single training sample for each person is enrolled in the database. In this case, many of the popular face recognition methods fail to work well due to the inability to learn the discriminatory information specific to the persons to be identified. To address this problem, in this paper, we propose an Adaptive Generic Learning (AGL) method, which adapts a generic discriminant model to better distinguish the persons with single face sample. As a specific implementation of the AGL, a Coupled Linear Representation (CLR) algorithm is proposed to infer, based on the generic training set, the within-class scatter matrix and the class mean of each person given its single enrolled sample. Thus, the traditional Fisher's Linear Discriminant (FLD) can be applied to SSPP task. Experiments on the FERET and a challenging passport face database show that the proposed method can achieve better results compared with other common solutions to the SSPP problem.
A 10Gb/s 4.1mW 2-IIR + 1-discrete-tap DFE in 28nm-LP CMOS
(2014)

A half-rate decision feedback equalizer (DFE) with two infinite impulse response (IIR) filters and one discrete-time tap is presented. The two IIR filters have different time constants to cancel the long tail of the pulse response. The discrete-tap cancels the first post-cursor inter-symbol interference term. The system can operate with a low transmit swing of 150mVpp-diff and 24 dB channel loss at the Nyquist frequency while consuming 4.1mW at 10 Gb/s. The receiver, including the DFE, clock buffers and clock phase adjustment, occupies an area of 8,760 μm2 and was fabricated in an ST 28nm LP CMOS process.
A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression
(2017)

Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.